Mining Natural Language Programming Directives with Class-Oriented Bayesian Networks

2008 ◽  
pp. 15-26 ◽  
Author(s):  
Manolis Maragoudakis ◽  
Nikolaos Cosmas ◽  
Aristogiannis Garbis
Keyword(s):  
Natural Language ◽  
Bayesian Networks ◽  
Natural Language Programming

Knowledge Based Expert Systems for CAD/CAM Experience of an EDM Application

1993 ◽  
Author(s):  
D. Kiritsis ◽  
Michel Porchet ◽  
L. Boutzev ◽  
I. Zic ◽  
P. Sourdin
Keyword(s):  
Natural Language ◽  
System Development ◽  
Constraint Propagation ◽  
Programming Environment ◽  
Knowledge Based ◽  
Natural Language Programming ◽  
Cad Cam ◽  
The One ◽  
Rule System ◽  
Cam Systems

Abstract In this paper we present our experience from the use of two different expert system development environments to Wire-EDM CAD/CAM knowledge based application. The two systems used follow two different AI approaches: the one is based on the constraint propagation theory and provides a natural language oriented programming environment, while the other is a production rule system with backward-forward chaining mechanisms and a conventional-like programming style. Our experience showed that the natural language programming style offers an easier and more productive environment for knowledge based CAD/CAM systems development.

scholarly journals How to automatically turn patient experience free-text responses into actionable insights: a natural language programming (NLP) approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Simone A. Cammel ◽  
Marit S. De Vos ◽  
Daphne van Soest ◽  
Kristina M. Hettne ◽  
Fred Boer ◽  
...  
Keyword(s):  
Natural Language ◽  
Patient Experience ◽  
Free Text ◽  
Natural Language Programming

scholarly journals Classifying Radiology Abstracts with Deep Learning

2021 ◽  
Author(s):  
George Shih ◽  
Hongyu Chen
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Natural Language ◽  
Language Processing ◽  
Breast Imaging ◽  
Machine Learning Techniques ◽  
Average Error ◽  
Ensemble Model ◽  
Radiological Society ◽  
Natural Language Programming

Background The Radiological Society of North America (RSNA) receives more than 8000 abstracts yearly for scientific presentations, scientific posters, and scientific papers. Each abstract is assigned manually one of 16 top-level categories (e.g. "Breast Imaging") for workflow purposes. Additionally, each abstract receives a grade from 1-10 based on a variety of subjective factors such as style and perceived writing quality. Using machine learning to automate, at least partially, the categorization of abstract submissions can result in saving many hours of manual labor. Methods A total of 45527 RSNA abstract submissions from 2014 through 2019 were ingested, tokenized, and pre-processed with a standard natural language programming protocol. A bag-of-words (BOW) model was used as a baseline to evaluate two more sophisticated models, convolutional neural networks and recurrent neural networks, and also evaluate an ensemble model featuring all three neural networks. Results ensemble model was able to achieve 73% testing accuracy for classifying the 16 top-level categories, outperforming all other models. The top model for classifying abstract grade was also an ensemble model, achieving a mean average error (MAE) of 1.01. Conclusion While the baseline BOW model was the highest performing individual classifier, ensemble models that included state-of-the-art neural networks were able to outperform it. Our research shows that machine learning techniques can, to a reasonable degree of accuracy, predict both objective factors such as abstract category as well as subjective factors such as abstract grade. This work builds upon previous research involving using natural language processing on scientific abstracts to make useful inferences that address a meaningful problem.

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